// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "paddle/phi/kernels/diagonal_kernel.h"

#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/full_kernel.h"
#include "paddle/phi/kernels/funcs/diagonal.h"
namespace phi {

template <typename T, typename Context>
void DiagonalKernel(const Context& dev_ctx,
                    const DenseTensor& x,
                    int offset,
                    int axis1,
                    int axis2,
                    DenseTensor* out) {
  if (x.numel() == 0) {
    Full<T, Context>(dev_ctx, out->dims(), 0, out);
    return;
  }

  auto* input = &x;
  const T* input_data = input->data<T>();
  auto input_dim = vectorize(input->dims());
  auto input_dim_size = input_dim.size();

  auto* output = out;
  T* output_data = dev_ctx.template Alloc<T>(output);
  auto output_dim = vectorize(output->dims());
  auto output_dim_size = output_dim.size();

  const int64_t offset_ = offset;
  int64_t axis1_ =
      static_cast<int64_t>(axis1 < 0 ? input_dim_size + axis1 : axis1);
  int64_t axis2_ =
      static_cast<int64_t>(axis2 < 0 ? input_dim_size + axis2 : axis2);

  std::vector<int64_t> input_stride = funcs::ComputeDimStride(input_dim);
  std::vector<int64_t> output_stride = funcs::ComputeDimStride(output_dim);

  int64_t out_numel = out->numel();
  for (int64_t idx = 0; idx < out_numel; idx++) {
    std::vector<int64_t> idx_dim(output_dim_size);
    int64_t temp = 0;
    for (size_t i = 0; i < output_dim_size; i++) {
      idx_dim[i] = (idx - temp) / output_stride[i];
      temp = temp + idx_dim[i] * output_stride[i];
    }
    int64_t tmp = idx_dim[output_dim_size - 1];
    std::vector<int64_t> list;
    list.clear();
    int64_t l = std::min(axis1_, axis2_);
    int64_t r = std::max(axis1_, axis2_);
    for (size_t j = 0; j < output_dim_size - 1; j++) {
      list.push_back(idx_dim[j]);
    }
    if (offset_ == 0) {
      list.insert(list.begin() + l, tmp);
      list.insert(list.begin() + r, tmp);
    } else if (offset_ > 0) {
      if (axis1_ < axis2_) {
        list.insert(list.begin() + l, tmp);
        list.insert(list.begin() + r, tmp + offset_);
      } else {
        list.insert(list.begin() + l, tmp + offset_);
        list.insert(list.begin() + r, tmp);
      }
    } else if (offset_ < 0) {
      if (axis1_ < axis2_) {
        list.insert(list.begin() + l, tmp - offset_);
        list.insert(list.begin() + r, tmp);
      } else {
        list.insert(list.begin() + l, tmp);
        list.insert(list.begin() + r, tmp - offset_);
      }
    }

    int64_t input_offset = 0;
    for (size_t i = 0; i < input_dim_size; i++) {
      input_offset = input_offset + list[i] * input_stride[i];
    }
    output_data[idx] = input_data[input_offset];
  }
}
}  // namespace phi
PD_REGISTER_KERNEL(diagonal,
                   CPU,
                   ALL_LAYOUT,
                   phi::DiagonalKernel,
                   float,
                   double,
                   int,
                   int64_t,
                   phi::complex64,
                   phi::complex128,
                   bool) {}